The segmentation of medical images is needed for computing clinically relevant measures of an object of interest from images, or for visualizing the objects of interest for better diagnosing or treating of patients. The segmentation of images means that an object of interest, such as an organ in medical imaging, is distinguished from the background of the image. Conventionally, the segmentation has been done manually or automatically. In the manual segmentation the user outlines the object, whereas the automatic segmentation alms at identifying the contours of the object by means of image processing. The drawback of the manual segmentation is that it is extremely time consuming. The number of images per patient examination is continuously increasing. For example, several hundreds of images can be acquired during one cardiac magnetic resonance (MR) imaging examination. However, the time a medical professional can use for the interpretation of the images has not increased correspondingly. Therefore, the time used per image is becoming notably short. The drawback of the automatic segmentation, on the other hand, relates to accuracy. The automatic segmentation, even though it is faster than the manual segmentation, can provide contours that deviate significantly from the actual contours of the object.
Lorenzo et al. in “Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm” (Medical Image Analysis 8(3) (2004) p. 255-256) disclose a segmentation tool for the segmentation of cardiac images. Lorenzo et al. use the expectation maximization algorithm and probabilistic atlas to segment left and right ventricle. Lötjönen et al. in “Statistical shape model of atria, ventricles and epicardium from short and long-axis MR images” (Medical Image Analysis 8(3) (2004) p. 371-386) and Assen et al. in “A 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data (Medical Image Analysis 10(2) (2006) p. 286-303) use image data from more than one imaging directions, typically acquired during cardiac studies. However, conventionally only two-dimensional short-axis images are segmented, and Simpson's rule for approximating definite integrals is used to compute the volumes.
Due to relatively high slice thickness in cardiac MR images, the modelling of, for example, apical and basal regions becomes difficult, and the use of several imaging directions makes the segmentation more accurate. The segmentation error of the methods of related art has been about two millimeters or more as the difference between two manually delineated segmentations has been slightly more than one millimeter.
What is needed is a solution for segmenting images of an object of interest. The solution should overcome the drawbacks of the state-of-art by providing a segmentation that is sufficiently robust, accurate and fast, so that it can be used in clinical practice.